Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data lay on a non linear manifold and are distributed across network nodes. Although numerous algorithms for distributed dimensionality reduction have been proposed, all assume that data reside in a linear space. In order to address the non-linear case, the authors introduce D-Isomap, a novel distributed non linear dimensionality reduction algorithm, particularly applicable in large scale, structured peer-to-peer networks. Apart from unfolding a non linear manifold, their algorithm is capable of approximate reconstruction of the global dataset at peer level a very attractive feature for distributed data mining problems.